转换试验用于评估omics数据中的性别差异
Julian K Christians1,2,3,4
1Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada.
Molecular human reproduction
|September 19, 2025
概括
性分层的奥米克分析往往产生虚假的结果. 转换试验有助于区分真实的性别特异性影响与偶然发现,改善对性别差异的数据解释.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
背景情况:
- Omics数据分析经常涉及性别分层,如果仅在一个性别中显著,则将影响报告为"性别特异性".
- 这种方法可以导致许多虚假的"性别特异性"效应,而没有显著的治疗性相互作用,特别是在大规模分子分析中.
研究的目的:
- 为了说明在欧米克数据分析中虚假的性别特异效应的问题.
- 引入和验证 permutation 测试作为一种方法来区分真正的性别特异性影响与偶然产生的影响.
- 为了提供工具,在omics研究中强有力的识别性别差异.
主要方法:
- 对RNA测序数据集的分析,以确定性别特异性治疗效应.
- 模拟具有已知的性别特异性基因效应的RNA测序数据集.
- 应用换试验来评估观察到的性别特异性影响的意义.
- 性别分层分析与相互作用分析的比较.
主要成果:
- 真实RNA测序数据在分层分析中显示了许多"性别特异性"效应,但很少有显著的治疗性相互作用.
- 在真实数据集中随机分配性别产生了比观察到的更多的"性别特异性"效应,表明了虚假的发现.
- 模拟数据与真正的性别特异性影响产生了显著的相互作用和性别特异性影响.
- 与相互作用分析相比,分层分析发现了更多的性别特异性影响,但也发现了更多的假阳性.
结论:
- 没有相互作用支持的性别分层分析可以在omics研究中产生误导性的结果.
- 转换试验提供了一种可靠的方法来验证性别特异性发现,并评估omics数据的可靠性.
- 提出的方法和提供的R代码可以提高识别分子数据中真实性别差异的准确性.
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